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Mathieu Gautier

Researcher at SupAgro

Publications -  125
Citations -  6794

Mathieu Gautier is an academic researcher from SupAgro. The author has contributed to research in topics: Population & Gene. The author has an hindex of 37, co-authored 120 publications receiving 5631 citations. Previous affiliations of Mathieu Gautier include Institut national de la recherche agronomique & University of Montpellier.

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DIYABC v2.0: a software to make approximate Bayesian computation inferences about population history using single nucleotide polymorphism, DNA sequence and microsatellite data

TL;DR: DIYABC v2.0 implements a number of new features and analytical methods, including efficient Bayesian model choice using linear discriminant analysis on summary statistics and the serial launching of multiple post-processing analyses.
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rehh: an R package to detect footprints of selection in genome-wide SNP data from haplotype structure

TL;DR: The R package rehh provides a versatile tool to detect the footprints of recent or ongoing selection with several graphical functions that help visual interpretation of the results.
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Genome-Wide Scan for Adaptive Divergence and Association with Population-Specific Covariates.

TL;DR: This study investigates several modeling extensions to improve the estimation accuracy of the population covariance matrix and all the related measures and defines a robust Bayesian framework to characterize adaptive genetic differentiation across populations.
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Reliable ABC model choice via random forests.

TL;DR: This work proposes a novel approach based on a machine learning tool named random forests (RF) to conduct selection among the highly complex models covered by ABC algorithms, modifying the way Bayesian model selection is both understood and operated.
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The effect of RAD allele dropout on the estimation of genetic variation within and between populations

TL;DR: It is found that ADO tends to overestimate genetic variation both within and between populations, and possible solutions to filter the most problematic cases of ADO using read coverage to detect markers with a large excess of null alleles are discussed.